Developing and Branding Your Analytics Communities

How can the principles of branding be applied to BI to help multiple groups in large organizations work collaboratively across departmental boundaries?

05/08/2012

By Mark Peco

The concept of business intelligence (BI) was first introduced in the late 1990s with a narrowly defined scope: fact-based information delivery to help managers improve their decision-making. This classic definition had a strong focus on technology and data. It was typically managed by information technology (IT) departments through data warehouse implementations.

As this original BI concept evolved over the next several years, a deeper understanding developed about its true business potential. This shift in thinking and definition is significant. In the modern context, BI is now more broadly defined as an organization's capability for "intelligent goal definition and attainment."

During this time, product branding and brand management have been successfully used by marketing groups to develop an identity, loyalty, community, sense of ownership, and emotional attachments to products and services. The goodwill created by a brand is a strong marketing driver that is used to drive sales and revenue.

Part of a brand is a memorable phrase or image that creates common emotions and associations for individuals from different backgrounds and in different demographic groups. Marketing and communication develop the desired emotional responses in the target market.

A professional baseball team draws its fans from a wide range of people with different interests, backgrounds, geographies, and other demographics. The team's brand is represented by their logo, uniform colors, and other icons. These help the team create a fan community sharing a common objective (winning) and a passion for athletic performance.

How can the principles of branding be applied to BI to help multiple groups in large organizations work collaboratively across departmental boundaries?

Implications for Your Organization

Large organizations are normally structured as vertical silos that support major business functions. Each silo is responsible for delivering results based on their functional role such as sales, accounting, production, engineering, or human resources. The theory of management states that individual results per silo should be additive across each area to achieve overall strategic outcomes, but the practice of management realizes that it is difficult to ensure that the discrete outcomes per silo are aligned and additive so as to achieve this goal. For example, the inability to work and collaborate across functional silos is a major difficulty that often leads to poor overall performance.

The modern definition of BI requires a horizontal focus across silos so information is delivered to those who need it to help generate useful insights that can drive their own performance in a manner complementary with the overall organizational objectives. This means that the classic definition of BI that was based strictly on information delivery concepts and supported by a technology department is not adequate. The potential of modern BI is realized only when information, insights, actions, results, and feedback become part of a unified approach.

The Rise of the Analytic Community

To understand how each vertical silo contributes to global objectives, informal structures arise to build the linkages, personal relationships, knowledge sharing, and expertise that can harness the analytical opportunities from the convergence of rising data volumes, inexpensive computing, and maturing software technologies. Unfortunately, formal organizational structures based on a functional model become barriers to collaboration, knowledge sharing, and insight that BI requires to make its impact.

An informal structure can be established as an "analytic community" that has a defined purpose in terms of business problems or opportunities. Participation in a community is based on common interests and needs, regardless of the department or management level of formal roles. The need for information content and the ability to analyze and act on it spans silos and management levels. An analytic community is defined by a specific analytical purpose, function, or objective; people choose to belong because they want to and because they are motivated by a business challenge.

Interest and capabilities in analytics should not be isolated within departments. Competencies for analytics include a wide range of skills and techniques that have traditionally been in the domain of individual disciplines such as marketing, finance, engineering, and customer service.

To achieve the modern vision of BI, all employees must be encouraged, guided, and coached to utilize and develop their own analytical skills and to share their insights. For example, techniques related to simulation, forecasting, planning, optimization, and statistics have existed in pockets for years. They must be harmonized across the silos. BI professionals have much to learn from each other and they have much to share. Combining the information management disciplines with the analytical disciplines into specific communities of interest can tear down the silos and foster analytical cultures across the organization.

To achieve the modern definition of BI, we must embed BI principles and practices within the core processes of our organization. This effort can be enabled by establishing a network of communities that drive, shape, and enable technology solutions, analytic capabilities, knowledge sharing, and business results. Branding can help enable this.

The Role of Branding

The term business intelligence is not always recognized or understood by practitioners working outside of the BI industry. Many planners, analysts, managers, engineers, designers, marketers, and accountants do not readily understand and embrace the term and how it relates to their work. Many business professionals view BI as a technology solution forced on them.

The potential of modern BI cannot be realized if it is not understood and embraced. We must harmonize existing analytics capabilities in fragmented pockets of our organization with managed information delivery solutions.

Fortunately, the underlying concept -- using information to drive business results -- is understood. Our major challenge is how to brand and describe these concepts into a broader footprint that resonates and is embraced by a diverse set of professionals in our organization.

An eco-system of analytic brands serving many communities of interest can emerge. Brands are valuable because people associate themselves with brands, be they consumer products or sports teams. Brands help people at many levels and in different functional areas with common interests associate their interests with something more specific than just the broad term BI.

A brand creates awareness, identity, loyalty, community, ownership, participation, and commitment. Brands remove the organizational boundaries and eliminate the "us-versus-them" mindset. A brand helps to create a common identify that supports the "we are all in it together" mindset, which helps foster collaboration and idea sharing. However, a brand can only develop through well-defined marketing and communications. A brand must be actively managed to ensure strong awareness and an associated emotional response.

Examples of Analytic Branding

A mature and modern BI program may exist as a network of communities of interest based on the analytic brand supported and promoted by each community. The decision makers and analysts within each community will resonate with their own brand that is supported by the broader BI principles.

Some examples of analytic brands are shown below.

Analytics Brand

Analytical Purpose

Members

Inventory Analytics

Optimize operational and financial issues related to purchasing and inventory management in a retail environment.

Attain goals related to optimum product inventory levels.

All staff responsible for purchasing, marketing, sales, and logistics functions.

Churn Reduction

Measure, monitor, understand, and reduce customer churn in a large telecomm environment.

Each analytics brand has a purpose, whether broad or narrow, based on organizational goals that require the support and alignment of multiple groups within an organization's vertical silos. Each analytic community is supported by a broader BI program that manages information delivery to fulfill their requirements. However, each community uses that information to generate insights, share knowledge, enable actions, monitor results, and achieve goals within their own areas of interest.

Just like the fans of a professional sports team come from all walks of life, they become bound together by the team's brand. An analytics' brand must be promoted and marketed across your organization to ensure that its membership base provides diversity and has a passion for results.

Mark Peco, CBIP, is a consultant and educator, holding degrees in engineering from the University of Waterloo. He is a partner with InQvis, a consulting firm based in Toronto, and is also a faculty member of The Data Warehousing Institute (TDWI). He can be contacted at mark.peco@inqvis.com.